Using the Best Linear Approximation With Varying Excitation Signals for Nonlinear System Characterization
For engineers needing to characterize nonlinear systems, this provides a practical black-box method to improve measurement accuracy, though it is an incremental improvement over existing approaches.
This work develops experimental methods to detect the internal structure of nonlinear systems using best linear approximation with varying excitation signals, achieving more precise frequency response measurements and reliable structure detection on two real systems with static nonlinear feedback.
Block oriented model structure detection is quite desirable since it helps to imagine the system with real physical elements. In this work we explore experimental methods to detect the internal structure of the system, using a black box approach. Two different strategies are compared and the best combination of these is introduced. The methods are applied on two real systems with a static nonlinear block in the feedback path. The main goal is to excite the system in a way that reduces the total distortion in the measured frequency response functions to have more precise measurements and more reliable decision about the structure of the system.